Software Engineer: ML Optimization

Generalist; Medford Hillside (MA); In loco; USD 180.000 - 240.000; Tempo pieno

Descrizione del lavoro

Generalist is seeking a skilled ML optimization engineer to advance the training and on-robot inference stacks. You will focus on making GPUs go faster, implementing ML, hardware, and software changes that drive step-function improvements, and pushing optimization across the entire inference and training pipeline.

The role demands keeping current with the latest ML techniques for transformer and diffusion-based architectures, and diagnosing bottlenecks from CUDA kernels to Python implementations.

Competenze

• Experience optimizing ML training and inference pipelines.
• Strong understanding of transformer and diffusion-based architectures.
• Proficiency with GPU/CPU bottlenecks and parallel computing.

Mansioni

• Make GPUs perform at peak to maximize throughput.
• Implement ML, hardware, and software changes that yield step-function gains.
• Optimize both inference and training stacks for efficiency and speed.

Conoscenze

ML optimization

CUDA kernels

Python PyTorch

Strumenti

CUDA

PyTorch

Descrizione del lavoro

About Generalist

At Generalist, we are on a mission to build general intelligence for the physical world and make it useful to everyone. We believe the industries and homes of the future will depend on humans and machines working together in new ways. Robots can help us build more and get more done.

We build embodied foundation models, starting with a focus on dexterity. This requires advancing the frontiers of data, models, and hardware, to enable robots to intelligently interact with the physical world.

The company embraces both large-scale AI and robotics as core to its DNA. Our team of researchers, roboticists, and company builders come from OpenAI, Boston Dynamics, Google DeepMind, and other frontier labs—with a track record of shipping AI breakthroughs. Before Generalist, we pioneered large embodied multimodal models and vision-language-action models (PaLM-E, RT-2, Gemini Robotics), launched and scaled ChatGPT and GPT-4 to hundreds of millions of users, engineered the foundations of autonomous driving, built next-generation robots (Atlas, Spot, Stretch) and pushed the limits of what they can do (from parkour to manipulation, and testing robustness).

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

About the Role

We internally call this team MBMB (More Big More Better). You will own optimizations on both the training and on-robot inference stacks. We are still in a regime of step-function, not incremental, gains.

You’ll be responsible for:

• Making GPUs go brrrrr
• Implementing ML, hardware, and software changes that lead to step-function gains
• Optimizing both the inference and training stacks

You might thrive in this role if you:

• Are proficient and stay current with the latest ML techniques for training and inference optimizations in transformer and diffusion based architectures
• Will chase ML optimizations anywhere: From the CUDA kernels, to ML architecture, to frontend or backend network bottlenecks, CPU bottlenecks, NVLink and comms, to torch, numpy, and Python inefficiencies.